Article
Engineering, Civil
Y. Sun, W. Bao, S. Qu, Q. Li, P. Jiang, Z. Zhou, P. Shi
Summary: This paper investigates the usefulness of the consider Kalman filter (CCKF) in hydrological modeling. The CCKF can improve the forecast performance of hydrological models by updating the states without updating the parameters. The results show that the CCKF is a more robust option for state estimation in the presence of parameter uncertainty.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Aerospace
Ankit Goel, Dennis S. Bernstein
Summary: In applications of state estimation, it is often necessary to restrict the state correction to a specific subspace corresponding to the measurement location. This paper presents the injection-constrained unscented Kalman filter (IC-UKF) and the injection-constrained retrospective cost filter (IC-RCF) to address this problem. The performance of these filters is evaluated numerically, and their accuracy and suboptimality relative to full-state output-error injection are compared.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2022)
Article
Automation & Control Systems
Zhidong Xu, Bo Ding, Tianping Zhang
Summary: This paper investigates the problem of event-based state and fault estimation for stochastic nonlinear systems with Markov packet dropout. By introducing fictitious noise, the fault is integrated into the system state, and a modified unscented Kalman filter (UKF) is proposed to estimate the state and fault. The stochastic stability of the proposed filter is also discussed, and two simulation results are presented to illustrate the performance of the proposed method.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Biochemical Research Methods
Amir H. Abolmasoumi, Mohammad Mohammadian, Lamine Mili
Summary: This paper proposes a revised version of the GM-UKF for state estimation in GRNs with different deviations from assumptions. The GM-UKF outperforms other methods for all outlier types, while the H-8-UKF is appropriate for changes in noise powers.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Davide Palitta, Jemima M. Tabeart
Summary: Algorithms for data assimilation aim to predict the most likely state of a dynamic system by combining information from observations and prior models. This paper introduces a weak-constraint four-dimensional variational data assimilation formulation, which can be understood as a minimization problem. One challenge lies in solving large linear systems of equations arising from the inner linear step of the chosen nonlinear solver. This paper proposes novel, efficient preconditioning operators involving the solution of certain Stein matrix equations, which improve computational performance and provide tighter bounds for the eigenvalue distribution of the preconditioned linear system.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Engineering, Electrical & Electronic
Runlong Xiao, Gang Wang, Lijun Fu, Fan Ma, Chun Li, Renji Huang, Xiaoliang Hao
Summary: An adaptive estimation method is proposed in this paper to ensure the accuracy of estimation and reliability of the algorithm by adaptively adjusting the estimation method according to changes in the system operating conditions, providing a solution to the electromagnetic transient issue caused by the periodic pulse load power changes in the medium-voltage DC integrated power system. The method is proven to be superior to existing methods in terms of estimative effect through experiments and is also validated for its reliability through consistency tests on the filter.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Civil
Shun Taguchi, Takayoshi Yoshimura
Summary: This study proposes a data assimilation method using a state space neural network for predicting non-recurring traffic congestion. The results show that the method achieves higher prediction accuracy for predicting unknown traffic congestion and is more robust against data sparsity.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Bernard Delyon, Qinghua Zhang
Summary: The Kitanidis filter is a natural extension of the Kalman filter and can be used for systems subject to arbitrary unknown inputs. It is designed by minimizing the trace of the state estimation error covariance matrix, and is shown to be optimal for the whole gain sequence in terms of matrix positive definiteness.
Article
Engineering, Aerospace
Gangqiang Li, Zheng H. Zhu
Summary: This study introduces a novel finite element Kalman filter for estimating the unmeasurable state of space tether systems. By combining real and virtual measurements, the full state is reconstructed and propagated in temporal space. Numerical analysis demonstrates the accuracy and effectiveness of the proposed method.
ADVANCES IN SPACE RESEARCH
(2021)
Article
Energy & Fuels
Guorong Zhu, Yihong Zheng, Xiangtian Deng, Jianghua Lu
Summary: The article presents an improved dual second-order observer for decoupled control and accurate estimation of the d-axis current, q-axis current, and rotor speed of the PMSM. The effectiveness and practicality of the proposed method are demonstrated through simulation and experimental results.
Article
Energy & Fuels
Yanbo Chen, Yuan Yao, Yuzhang Lin, Xiaonan Yang
Summary: This paper proposes a dynamic state estimation method based on a Kalman filter for the unified scheduling and control of integrated electricity-gas systems. By considering the dynamic characteristics of natural gas pipelines and fusing measurements with different sampling periods using the interpolation method, high-precision operation data filtering is achieved.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2022)
Article
Chemistry, Physical
Lukas Boehler, Daniel Ritzberger, Christoph Hametner, Stefan Jakubek
Summary: This paper presents an alternative approach to extended Kalman filtering for polymer electrolyte membrane fuel cell systems, providing robust real-time state estimations and achieving faster computational speed compared to standard approaches. The method resolves dependencies on operating conditions and offers accurate state estimates even in challenging scenarios, making it a viable option for control and fault detection applications.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Robotics
Seyed Fakoorian, Angel Santamaria-Navarro, Brett T. Lopez, Dan Simon, Ali-akbar Agha-mohammadi
Summary: This work presents a resilient and adaptive state estimation framework, AMCCKF, for robots operating in perceptually-degraded environments, which is able to robustly handle corrupted measurements and adjust filter parameters online for improved performance. Two methods are developed, modifying noise models and kernel bandwidth based on measurement quality, with differences in computational complexity and overall performance. The framework is validated through real experiments on aerial and ground robots, forming part of the solution used in the DARPA Subterranean Challenge by the COSTAR team.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Water Resources
Andrew Pensoneault, Witold F. Krajewski, Nicolas Velasquez, Xueyu Zhu, Ricardo Mantilla
Summary: This paper discusses the application of data assimilation techniques in hydrology, focusing on the potential of EnKF and its extensions in sequential state estimation and Bayesian inverse problems. The authors improve the streamflow in a virtual catchment using the EKI algorithm and demonstrate its favorable performance.
ADVANCES IN WATER RESOURCES
(2023)
Article
Geosciences, Multidisciplinary
Jagat S. H. Bisht, Prabir K. Patra, Masayuki Takigawa, Takashi Sekiya, Yugo Kanaya, Naoko Saitoh, Kazuyuki Miyazaki
Summary: Methane (CH4) is the second major greenhouse gas and its increase in the atmosphere raises concerns about sustainability and climate change. A data assimilation system using a local ensemble transform Kalman filter (LETKF) is developed to estimate surface emissions of CH4. The performance of the system is tested and optimized using simulated observations and three covariance inflation methods. The results show that the RTPS covariance inflation method performs better and produces estimates that are consistent with the true values.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2023)